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Gradient boosting algorithm for predicting student success.

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Title: Gradient boosting algorithm for predicting student success.
Authors: Jabir, Brahim1 ibra.jabir@gmail.com, Merzouk, Soukaina1 merzouk.soukaina@gmail.com, Hamzaoui, Radoine2 info.hamzaoui@gmail.com, Falih, Noureddine2 nourfald@yahoo.fr
Source: International Journal of Electrical & Computer Engineering (2088-8708). Aug2025, Vol. 15 Issue 4, p4181-4191. 11p.
Subjects: Machine learning, Moodle (Computer software), Online education, Prediction models, Academic achievement, Boosting algorithms
Abstract: The idea of using machine learning resolution techniques to predict student performance on an online learning platform such as Moodle has attracted considerable interest. Machine learning algorithms are capable of correctly interpreting the content and thus predicting the performance of our students. Algorithms namely gradient boosting machines (GBM) and eXtreme gradient boosting (XGBoost) are highly recommended by most researchers due to their high accuracy and smooth boosting time. This research was conducted to analyze the effectiveness of the XGBoost algorithm on Moodle platform to predict student performance by analyzing their online activities, practicing various types of online activities. The proposed algorithm was applied for the prediction of academic performance based on this data received from Moodle. The results demonstrate a strong correlation between many activities like the number of hours spent online and the achievement of academic goals, with a remarkable prediction rate of 0.949. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Moodle+%28Computer+software%29%22">Moodle (Computer software)</searchLink><br /><searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink>
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  Data: The idea of using machine learning resolution techniques to predict student performance on an online learning platform such as Moodle has attracted considerable interest. Machine learning algorithms are capable of correctly interpreting the content and thus predicting the performance of our students. Algorithms namely gradient boosting machines (GBM) and eXtreme gradient boosting (XGBoost) are highly recommended by most researchers due to their high accuracy and smooth boosting time. This research was conducted to analyze the effectiveness of the XGBoost algorithm on Moodle platform to predict student performance by analyzing their online activities, practicing various types of online activities. The proposed algorithm was applied for the prediction of academic performance based on this data received from Moodle. The results demonstrate a strong correlation between many activities like the number of hours spent online and the achievement of academic goals, with a remarkable prediction rate of 0.949. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.11591/ijece.v15i4.pp4181-4191
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      – Code: eng
        Text: English
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Moodle (Computer software)
        Type: general
      – SubjectFull: Online education
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      – SubjectFull: Prediction models
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      – SubjectFull: Academic achievement
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      – SubjectFull: Boosting algorithms
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              Text: Aug2025
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